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Over a single question: “How much effort did you personally have to put forth to handle your request?” the conventional survey method seeks to measure Customer Effort Score (CES). In a bid to answer this question, respondents are asked to rate their effort on a scale of 1 to 5. The lower customers rate their effort, the better it is considered to be. This is based on a conviction that by reducing customer effort, organizations increase customer loyalty.
Instead of asking customers about their effort involved on a 1 to 5 scale, Interaction Analytics embroils measuring CES by analyzing customer interaction data. Each customer interaction over phone, email and chat is utilized to score customer effort. The interaction data is further evaluated to identify and address issues leading to high effort across numerous touch-points.
On the other hand, a CES survey is meant to collect only customer effort rating on a scale of 1 to 5. Survey scores tell whether the effort is high or low. But, these scores by themselves cannot reveal the root causes of high or low effort. Furthermore, a CES survey alone cannot help detect processes that are forcing customers to call the contact center or switch from one channel to another. Also, conducting a separate survey for gauging CES can be a time-consuming and costly affair.
Since interaction analytics eliminates the need to run a distinct CES survey, it occurs to be a better option in terms of cost-efficiency speed, and results. 100% of customer interactions are evaluated to identify customer sentiments and phrases to gauge customer effort across various touch-points and discover areas where customers are exerting undue effort. Thus, factors like inclusiveness, problem identification, root cause analysis, and accuracy make interaction analytics a better way than the survey method to measure and reduce CES.
The entire process of scoring customer effort with interaction analytics will broadly involve the following steps:
Identify negative customer emotions: Interaction analytics analyzes customer interactions to pin down negative customer emotions. Emotions expressing customers’ anger, frustration or dissatisfaction with the customer service or self-service channels are detected during this phase.
Correlate negative emotions with customer effort across touch-points: These identified emotions are then linked to effort types that customers are unnecessarily exerting across different touch-points to address their needs or issues.
Quantify the degree of customer effort based on the severity of expression. The degree of effort types is then quantified based on the severity and frequency of emotions expressed by customers during interactions.
Get an organization-wide customer effort score on a scale of 1 to 5: According to the degree of effort, each effort type is ranked in an ascending order and given a score. Overall organization-wide CES is then calculated on a scale of 1 to 5 by summing up the value of all effort types and dividing the aggregate value by the total number of effort types.
Interaction analytics is a more comprehensive and efficient way to calculate and reduce CES. Thus in an attempt to improve customer experience, satisfaction and loyalty, organizations must leverage this groundbreaking technology. By making it easier for customers to interact or transact, organizations can take their business performance to newer heights.
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Source by Sheetal Kumari
